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Title: Lindahl Lecture 1: The Economics of Cities


1
Lindahl Lecture 1 The Economics of Cities
  • Edward L. Glaeser
  • Harvard University

2
Cities Result from Three Forces
  • Agglomeration Economies and Social Interactions
  • These are the magic of urban areas
  • Urban Technologies Bricks and Mortar, Trains and
    Cars
  • Government Policies
  • Both local and national

3
The Plan of these Lectures
  • In lecture 1, I will focus on agglomeration
    economies what makes cities productive and
    attractive.
  • In lecture 2, I will focus on social interactions
    and other effects of proximity
  • In lecture 3, I will address both the urban
    technologies and the role of government

4
Plan of this Lecture
  • Overview of Urban Economics
  • Measuring Agglomeration based on location
    patterns
  • Lessons from Urban Growth
  • Urban Labor Markets
  • Learning, Information and Cities some theory

5
The Heart of Urban Economics Spatial Equilibrium
  • Workers must be indifferent across space
  • U(Wages, Amenities, Prices)U
  • Higher wages must be offset be either lower
    amenities or higher prices.
  • Firms must be as well Profits(Wages, Prices,
    Productivity)0
  • Higher wages must be offset by either higher
    productivity or higher prices.
  • There is also a housing supply equilibrium that
    will be addressed in lecture 3.

6
An Easy Example
  • Assume wages are fixed at w and that commuting
    costs equal tdistance from the city then
    spatial equilibrium implies that rents must
    decline by tdistance from city.
  • Rents or housing values will be higher in areas
    with higher amenities or better schools.

7
Housing Prices and Temperature 1990
8
Why locate together?
  • Cities can come in principle for two reasons
  • First, a desire to be next to some exogenous
    attribute, like a mine or a port
  • Second, a desire to be next to the other
    inhabitants of the city

9
Why Cities?
  • As such, cities are defined as the absence of
    physical space between people and firms
  • They always occur in an attempt to eliminate
    transportation costs for goods, people and ideas
  • The empirical questions revolve around which are
    these are more important

10
Moving Goods, People and Ideas
  • Cities are originally about moving goods
  • Every large city in the U.S. before 1880 is on a
    river and most are where the river meets the sea.
  • Local Feedback where producers move to be close
    to consumers (Krugman).

11
Moving People
  • Modern big cities specailize in business
    services these require fact to face contact.
  • Cities allow works to switch employers and
    industires, which provides insurance and better
    search.
  • Proximity to other people isnt just productive,
    its also fun (city as marriage market).

12
Moving Ideas
  • Ideas, like everything else, move better over
    short distances (face-to-face)
  • Jaffe, Trajtenberg and Henderson show that patent
    citations are geographically localized.
  • Idea-intensive industries (finance, the arts)
    remain core parts of urban growth.
  • Urban edge in idea production makes cities
    important (Athens, Florence).

13
The Impact of Proximity
  • While city location is a choice, it is also
    interesting because it shapes outcomes
  • Firms may be more productive in dense areas
    (Ciccone and Hall, 1996)
  • Workers may learn more quickly in dense areas
  • It may be easier to steal in dense areas
  • Our beliefs are formed by our neighbors

14
Measuring Agglomeration (Ellison, Glaeser, JPE,
1997)
  • How should we measure the amount of agglomeration
    or people or industry?
  • I think measures should generally be
    model-driven, i.e. reflect a parameter in some
    sort of a model.
  • Assume profits have the following form

15
Where we assume
  • Individual shocks follow a weibull distribution
  • The spillover effect takes on a value of 1 with
    probability gs
  • The mean and variance of profits are

16
Together these assumptions give us that
17
Properties of the Index
  • Easy to compute with available data
  • Easy benchmark with no spillover/natural
    advantage version
  • Comparable across industries with different sizes
    of firms
  • Comparable across different levels of aggregation
  • Not good at dealing with issues of actual location

18
Facts on agglomeration
  • Median estimate of gamma is .026 mean is .051.
  • A few industries are extremely concentrated fur
    goods (.6), costume jewelry (.3)
  • Many are not cane sugar refining,
  • A few really change when we correct for plant
    size (vaccuum cleaners)

19
Does Natural Advantage Explain Agglomeration
(AER, 1999)
  • To extend the JPE paper, we try to control for
    local characteristics

20
What do the variables mean?
  • The delta is industry specific and allows
    different industries to respond differently to
    costs
  • The beta is the coefficient to estimate that is
    cost specific (i.e. electricity, labor, etc.)
  • The y variables are state cost specific (i.e.
    price of electricity in kansas)
  • The z variables are industry cost specific (i.e.
    how much does that industry use that input)

21
The Empirical Strategy
  • Regress state/industry shares on characteristics
    and then ask how much is explained
  • Characteristics include energy prices, labor
    costs, proximity to the coast, proximity to
    consumers, etc.
  • Some are quantities some are prices.

22
Overall Results
  • Controlling for all of these variables reduces
    the mean gamma across industries from .051 to
    .048.
  • When we allow 2 and 3 digit industry dummies (we
    are using 4 digit industries) concentration falls
    to .045 and .041
  • Since many industries arent very concentrated,
    this explains some portion of those industries,
    but little of the highly concentrated industries.

23
The Dynamics of Industrial Concentration (REStat,
2002)
  • How permanent are concentrations of industries
    Krugman (1991), e.g.

24
Empirical Results
  • Use the Census Longitudinal Research Database
    with plant level data for all manufacturing
  • Estimates of beta are around -.06 for 5 year
    patterns
  • Mean reversion would cause concentration to
    decline by 12 percent every 5 years,
  • But this is made up for by the concentration of
    new firms

25
An Extension New Births, Closures, Etc.
  • We can extend the methodology to look at what
    sort of changes create mean reversion
  • Closures are more likely in places with initial
    concentration
  • New Openings are more likely in places with less
    initial concentration (equally of affiliated and
    unaffiliated plants)

26
Co-Agglomeration (NBER Working Paper, 1997,
Dumais E/G)
  • To add to our knowledge of the sources of
    agglomeration, we look at which industries
    colocate near one another.
  • Changes specification regress growth in
    employment on presence of other industries in
    initial period.
  • Levels specification regress employment on
    presence of other industries (BUT THERE IS A
    REFLECTION PROBLEM)
  • All measures of colocation are normalized to have
    standard deviation of 1

27
Suppliers and Consumers
  • Use Input-Output matrices to calculate the extent
    that an industry buys to or sells from other
    industries.
  • Use that matrix to calculate the extent that a
    state or MSA is supplier or customer
  • In levels, .06 for customers, .01 for suppliers.
  • In State changes, .04 and .03
  • In MSA changes, .02 and 0

28
Labor Supply
  • Use occupation data to figure out who uses the
    same type of workers
  • Calculate a similarity index across and ask which
    places have industries that use similar workers
  • In state levels, the coefficient is .41
  • In MSA changes, the coefficient is.43
  • In State Changes, the coefficient is .18

29
Idea Flows
  • Option 1 Use the Scherer input output matrix for
    patent flows
  • Option 2 Use patents of co-ownership, excluding
    those firms with supply/demand relationship
  • In levels, the coefficients are .04 and .03
  • State change coefficients are -.01 and .06
  • MSA changes coefficients are 0 and .08

30
Urban Growth Underpinnings
31
  • Worker utility equals CN-mw U, where C is a
    consumption amenity index.
  • There is a fixed supply of Z in the city denoted
    Z.
  • Using the first order condition for firms, and
    these two conditions then gives us, using the
    notation that lnxx

32
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33
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34
This implies that
  • City size will be a function of consumer
    amenities, fixed factors of production,
    reservation utility levels and so forth.
  • Wages will also rise with productivity this is
    being offset by lower amenity levels.
  • These equations are then first differenced to
    provide estimating equations

35
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36
To close the model
  • Assume changes in A and changes in C are
    functions of initial characteristics and then
    regress changes in population (or employment) and
    income on initial conditions.
  • Higher employment means either more productivity
    or better amenities
  • Higher wages means either more productivity or
    worse amenities.

37
Testing the New Growth Theory (GKSS, JPE, 1992)
  • Under what conditions are new ideas created?
  • Marshall/Arrow/Romer high concentration, big
    firms
  • Jacobs diversity, lots of little firms
  • Michael Porter high concentration little firms

38
The Empirical Test
  • Using city-industry (i.e. steel in Pittsburgh)
    growth between 1956 and 1987, GKSS look at what
    predicts growth
  • Concentration of the industry (share in city
    relative to share in U.S.)
  • Initial Employment in the City-Industry
  • Competition (or firm size relative to national
    average)
  • Diversity in other industries

39
Results
  • Average firm size (competition) always predicts
    more growth what does it mean?
  • There is substantial mean reversion
  • Relative size is sometimes good/sometimes bad (no
    clear pattern)
  • Diversity is good in our paper (not obvious how
    robust)

40
Subsequent Work Climate and the Consumer City
  • In 1900, cities had to locate in places where
    firms had a productive advantage.
  • In 2000, cities increasingly locate in places
    with attractive amenities.
  • The move to warm, dry places.
  • The continued resilience of a few big consumer
    cities (NYC, Chicago, Boston, San Francisco).

41
Climate is the most reliable predictor of city
growth
42
Best thought of as a regional effect
43
Other correlations between pop. growth and
consumer amenities
  • 35 percent correlation with temperature and 12
    percent with dryness
  • 24 percent correlation with proximity to ocean
    (Rappaport Sachs)
  • 14 percent correlation with theaters
  • In France, 45 percent correlation with
    restaurants and 33 percent with hotel rooms
  • In UK, 31 percent correlation with tourist nights

44
Other facts
  • Real wages used to decline with city size, now
    they rise (to be discussed later)
  • Amenities (high housing prices relative to wages)
    strongly predict later population growth
  • Housing price growth in central cities has boomed
  • Reverse Commuting has increased

45
Urban Growth is Very Persistent
46
The Rise of the Skilled City (JME, 1995, BWPUA,
2004)
  • One fact that is regularly observed is the more
    skilled cities grow more quickly (Cityscape,
    1994)
  • Simon and Nardinelli show this going back to
    1880.
  • Are skilled cities more innovative?
  • Is the productive value of being around skilled
    workers rising?

47
What does the rise of the skilled city mean?
  • Or, perhaps are skilled cities become more
    attractive places to live?
  • Test using wage changes, housing price changes
    and income changes
  • The skill premium (i.e. the extra wages
    associated with being around skilled people) are
    rising quickly
  • Housing prices rise almost enough to keep real
    wages constant

48
Skills and City Growth
49
Also predicts growing income
50
Interpretation
  • The natural interpretation of this is that skills
    are working through labor demand, not labor
    supply.
  • But it is true that the skills effect still works
    within metro areas (which are common labor
    markets)
  • One startling fact is that skills matter for
    older, colder places, not newer warmer cites the
    reinvention hypothesis

51
Declining Regions
52
Growing Region (the West)
53
The Reinvention Hypothesis
  • An alternative interpretation skills matter in
    times of shock (Schultz, Welch).
  • Skilled cities excel because they permit
    innovation.
  • As such, the key to reinvention is to keep
    skilled people from leaving.

54
Bostons Growth is one of Reinvention
  • In 1630, Winthrop comes to Boston for
    consumption, not production reasons.
  • City on the Hill-- a religious community.
  • All other colonies are about production.
  • Original export industry is some fishing and
    selling goods to new immigrants.

55
Americans first city
  • Boston is founded in 1630 with 150 settlers.
  • Location is determined by the Charles river and
    clean water.
  • Population rises to 7,000 in 1690.
  • Population is 17,000 is 1740 when the city is
    overtaken by Philadelphia.

56
The 1640 Crisis and Its Resolution
  • In the early 1640s, the flow of immigrants
    subsides.
  • English revolution
  • Bostonians respond by reinvention, not exit.
  • Respond by selling basic foodstuffs and wood, but
    now to other colonies.

57
The Colonial Model for Boston
  • New England exported to other colonies
  • 73 percent to the Southern Colonies and Caribbean
    (1770)
  • 13 percent to England
  • Goods were basic commodities
  • 35 percent is fish (to West Indies 1770)
  • 32 percent livestock
  • 21 percent woodstock

58
Basic Model
  • Land in Virginia and Haiti is worth more growing
    tobacco and sugar
  • The North has little it can export to Europe, so
    its land is worth less and it grows commodies.
  • North is poorer than South in the 1700s.

59
The 19th Century Reinvention
  • But after 1790, Boston begins to grow again.
  • Growth from 18,000 in 1790 to 90,000 in 1840
  • Kept pace with national population growth.
  • It is maritime, not manufacturing.
  • 10,000 in maritime trades
  • 5,000 in manufacturing (less than Lowell)

60
Boston as a Share of the U.S.
61
What Happened?
  • Bostons port is still inferior to NYC.
  • Between 1821 and 1841, Bostons share of trade
    drops from 21 percent to 10 percent.
  • But Bostonians increasingly own and man the
    ships.
  • Bostons share of registered tonnage rises from
    45 to 58 percent between 1811 and 1851.
  • Yankees captures New York Port around 1820 and
    dominated its activity until the Civil War
    (Albion, 1931).

62
What Happened, Continued
  • Bostons comparative advantage was in human
    capital both at the high end (merchants) and in
    sailers.
  • Over the 1790-1840 period, technology and
    politics increased globalization of trade.
  • China trade and South Africa
  • Whaling far from New England
  • Clipper Ships
  • The human capital became more important than the
    port location.

63
Live by the Clipper Ship, Die By
  • In the 1840s, steam ships start becoming more
    important than sail.
  • Bostons human capital becomes far less valuable.
  • Bostons loses it maritime dominance, never to
    regain it.

64
But Reinvention Once Again
  • Over the 1840-1920 period, Boston would continue
    to boom.
  • Manufacturing replaced maritime.
  • Improvements in engine technology helped the city
    in two ways
  • Freed Manufacturing form river power
  • Created Rail Networks

65
And then theres the Irish
  • Boston starts becoming Irish in the 1840s.
  • The Potato famine coincides with last era of
    Boston maritime dominance.
  • As a result, its cheaper for the Irish to go from
    Liverpool to Boston than to NYC this will not be
    true for later migrants.

66
The Twentieth Century
  • Manufacturing left cities
  • Car cities replaced higher density areas
  • People fled cold places
  • The rich fled redistributive cities.

67
In the 1970s, Boston was in bad shape
  • Population had been declining for decades
  • The economy was in shambles
  • Housing cost less than new construction in most
    of the area.

68
But since 1980, the city has surged
  • Population has grown modestly
  • The economy has grown robustly
  • Housing prices have soared.

69
Economic Growth since 1980
  • In 1980, per capita income is the Boston Metro
    Area was 7547 which meant it ranked 61st in the
    nation.
  • In 1994, personal income was 26,093 tenth in the
    nation.
  • In 1996, average annual pay was 34,383, sixth in
    the nation.

70
Middlesex County Employment
  • Professional, Scientific and Technical Services
    110,000 jobs or 13 percent
  • Educational Services 64,000 jobs or 7 percent
  • Administrative and Support Services 64,00 jobs
    or 7 percent
  • Computer and Electronic Manufacturing 58,000
    jobs or 7 percent/

71
Urban Wages (JOLE, 2001)
  • Wages are higher in big cities than in small
    towns
  • This is a nominal wage difference, not a real
    wage difference
  • There is no labor supply puzzle, but there is a
    labor demand puzzle. b

72
Nominal Wages and City Size(Slope.073,
R-Squared.3)
73
Real Wages and City Size 1970
74
Real Wages and City Size Today
75
Is it Selection?
  • Wage Premium for metropolitan area residence
    .2-.35 depending on source
  • What about the real wage facts today
  • Controlling for standard omitted factors
    (education, industry, occupation) makes little
    difference
  • Controlling for AFQT in the NLSY makes no
    difference

76
More on selection
  • Parents location when used as an instrument
    predicts higher wages today.
  • But individual fixed effects regressions do
    generally eliminate much of the city effect
  • .28 to .05 in PSID
  • .24 to .1 in NLSY
  • Whats going on here?

77
The Learning Hypothesis
  • If cities increase human capital only slowly,
    then this can explain the individual fixed effect
    results without selection
  • Urban dummy is small for young workers (under 10
    percent)
  • But rises more than 15 percent over time
  • Also true in fixed effect regressions a 15
    percent increase over time

78
Analysis of Movers
  • Ashenfelter dip before leaving or moving to
    cities
  • 7 percent gain or so within a few years
  • Increasing wage gains over time
  • The NLSY results show somewhat quicker wage
    growth
  • People who leave cities dont face wage losses.

79
Learning in Cities (Journal of Urban Economics,
1999)
  • To understand the previous section, a brief model
    with two skill levels (the paper does a more
    general distribution).
  • Your probability of becoming skilled involves (1)
    meeting a skilled person in your industry and (2)
    imitating that person (with prob. C)
  • If the share of skilled people in an area is s,
    then the probability of becoming skilled from
    each interaction is cs/I.

80
More on learning
  • The key assumption is that the number of meetings
    is a function of city size or density, or D(N)
    where N is population.
  • The probability of becoming skilled in a period
    equals 1-(1-cs/I)D(N)
  • If city renttransportsaN/2, and unskilled
    wagesw and the gain from being skilled is V then

81
Closing the learning model
  • Spatial equilibrium requires
  • (1-(1-cs/I)D(N) )V-aN/2(1-cs/I)D(N) V-aN/2
  • The gains from extra learning are offset by
    higher rents.
  • If there are just two locations one with no
    learning and the other, a city then

82
Comparative Statics
  • City size rises with returns to learning,
    discount factor and falls with A.
  • The skill level of the city will itself also be a
    function of the learning parameters.
  • With multiple skill levels, the skill
    distribution is uniform.

83
Information Technology and the Future of Cities
(Journal of Urban Economics, 1998)
  • So cities exist in part to speed information
    flows
  • Doesnt that mean that information technology
    will kill cities?
  • Not so fast the key question is whether
    face-to-face interactions and electronic
    interactions are complements or substitutes

84
A Simple Model
  • Step 1 learn reservation value (denoted j with
    cumulative distribution R(j) and choose whether
    or not to collaborate
  • Step 2 learn match quality a, which means match
    returns are af(i) where i is intensity
  • Step 3 produce intensity using elecronic media
    (phones) or face-to-face

85
Phones vs. Face-to-Face
  • Two technologies differ in their fixed costs and
    in their power
  • iPBPT and ifBf(T-T), where BfgtBp
  • Phones dont have fixed costs, but they are worse
    at creating intimacy.
  • Use phones whenever desired i is low.

86
Solving the model
  • There are two cutoff values for a
  • The lower values determines a level of a at which
    is makes sense to end the relationship
  • A higher values above which people use face to
    face interactions
  • Better electronic technologies are increases in
    BP, which impacts several margins

87
Improvements in Technology
  • First it decreases the cutoff of a at which you
    interact at all.
  • Second, it increases the cutoff at which you
    switch from phones to face-to-face.
  • Third, it lowers the cutoff for the initial
    participation decision.

88
What does this mean?
  • First, improvements in technology may actually
    increase the amount of face-to-face contact, by
    increasing the number of people who work
    together.
  • Second, if cities are a technology for lowering
    the fixed costs of face-to-face, then demand for
    cities will rise if improvements in technology
    raise face-to-face contact.
  • Third, the key condition for this to hold is that
    people in cities use phone technology more.

89
What does the data say?
  • Fact 1 Phones and cities go together across
    countries, and over time.
  • Fact 2 Business travel has risen over the past
    20 years (face to face)
  • Fact 3 Co-authorship and other forms of
    interaction are rising steadily.
  • Fact 4 High tech industries are particularly
    likely to urbanized

90
More on the data
  • Fact 5 Silicon Valley is clustered
  • Fact 6 People in cities often use electronic
    forms of interaction more, not less.
  • Overall there is no compelling case that cities
    and technology are complements, but none that
    they are substitutes either.
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